Software-Defined Infrastructure for IoT-based Energy Systems

Author

Stephen Lee, University of Massachusetts AmherstFollow

Author ORCID Identifier

https://orcid.org/0000-0001-9022-4259

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded

2019

Month Degree Awarded

September

First Advisor

Prashant Shenoy

Second Advisor

David Irwin

Third Advisor

Ramesh Sitaraman

Fourth Advisor

Deepak Ganesan

Subject Categories

OS and Networks | Other Computer Sciences | Power and Energy | Theory and Algorithms

Abstract

Internet of Things (IoT) devices are becoming an essential part of our everyday lives. These physical devices are connected to the internet and can measure or control the environment around us. Further, IoT devices are increasingly being used to monitor buildings, farms, health, and transportation. As these connected devices become more pervasive, these devices will generate vast amounts of data that can be used to gain insights and build intelligence into the system. At the same time, large-scale deployment of these devices will raise new challenges in efficiently managing and controlling them. In this thesis, I argue that the IoT devices need programmability and need to provide software controls in order to manage them efficiently. Further, it will need data-driven modeling techniques to process and analyze a vast amount of data from heterogeneous devices to derive actionable insights. My thesis explores the problems posed by software-defined IoT energy infrastructure. I present four techniques that use systems and machine learning principles to design, analyze and deploy the next generation of smart IoT energy systems. First, I discuss how current state-of-the-art LIDAR-based approaches in identifying ideal locations on rooftops for deploying energy systems such as solar do not scale to many regions of the world. To address the challenges, I propose DeepRoof, a data-driven approach that uses deep learning to estimate the solar potential of roofs using satellite imagery and identify ideal locations for installation. We evaluate our approach on different types of roof and show that our technique is comparable to LIDAR-based methods. Second, I study how excessive solar can cause problems in the grid and examine how programmatic control of the solar output can prevent congestion in the electric grid. Further, I present a decentralized approach that can control the solar arrays in a grid-friendly manner. Also, my approach provides flexible control of solar output, and I show that such mechanisms allow for higher solar penetration in the grid. Third, I discuss the challenges in community-owned (and shared) distributed energy resources that do not provide independent control to users. To do so, I propose vSolar, an approach to virtualize the solar arrays and energy storage that allows independent control. Further, I show how using vSolar users can exercise independent control, implement their custom energy sharing policies, and reduce energy costs through energy trading. Finally, I present the challenges, and the high throughput needs to enable a peer-to-peer energy trading platform using permissioned blockchains. I propose FabricPlus, an enhanced Hyperledger Fabric blockchain, that contains a series of optimizations to enable high throughput transactions. FabricPlus increases the transaction throughput many folds, without requiring any changes to its external interfaces. I also show considerable performance improvement over the baseline Fabric.

DOI

https://doi.org/10.7275/15149241

Recommended Citation

Lee, Stephen, "Software-Defined Infrastructure for IoT-based Energy Systems" (2019). Doctoral Dissertations. 1747.
https://doi.org/10.7275/15149241 https://scholarworks.umass.edu/dissertations_2/1747

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.